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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Related Experiment Video

Updated: Oct 29, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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PseudoGA: cell pseudotime reconstruction based on genetic algorithm.

Pronoy Kanti Mondal1, Udit Surya Saha1, Indranil Mukhopadhyay1

  • 1Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, West Bengal, India.

Nucleic Acids Research
|July 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces PseudoGA, a novel genetic algorithm for ordering cells in single-cell RNA sequencing (scRNA-seq) data. PseudoGA accurately estimates cell pseudotime trajectories, revealing dynamic gene expression changes during transient cell states.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Dynamic gene expression regulation occurs through transient cell states, which bulk RNA sequencing (RNA-seq) cannot capture.
  • Single-cell RNA sequencing (scRNA-seq) enables the reconstruction of cell differentiation trajectories, termed 'pseudotime', to study dynamic transcriptomic changes.
  • Current pseudotime estimation methods often rely on dimensionality reduction, potentially leading to information loss.

Purpose of the Study:

  • To develop a robust and accurate method for pseudotime estimation from scRNA-seq data.
  • To address the limitations of existing pseudotime inference techniques, particularly information loss due to dimensionality reduction.
  • To provide a computationally efficient and adaptable tool for analyzing dynamic gene expression in single cells.

Main Methods:

  • Proposed PseudoGA, a genetic algorithm-based approach for ordering cells along a pseudotime trajectory.
  • Assumed that gene expression varies smoothly along the pseudotime continuum.
  • Evaluated method accuracy using simulated and real scRNA-seq datasets.

Main Results:

  • PseudoGA demonstrated superior accuracy in pseudotime estimation compared to existing methods.
  • The method proved robust across simulated and benchmarking real datasets.
  • PseudoGA showed no dependence on dimensionality reduction techniques, preserving more biological information.

Conclusions:

  • PseudoGA offers a robust and accurate approach for pseudotime estimation in scRNA-seq data.
  • Its generality and independence from dimensionality reduction make it a versatile tool for uncovering dynamic biological processes.
  • PseudoGA is time-efficient and suitable for large-scale scRNA-seq analyses, with R code available.